Update app.py

app.py

@@ -13,29 +13,31 @@ import os
 from datetime import datetime
 from dotenv import load_dotenv
 
+
 # Load environment variables
 load_dotenv()
 
 # Configure Gemini API
 GEMINI_API_KEY = os.getenv("gemini_api")
+if not GEMINI_API_KEY:
+    raise ValueError("GEMINI_API_KEY environment variable not found")
+
 genai.configure(api_key=GEMINI_API_KEY)
 generation_config = {
-  "temperature": 1,
-  "top_p": 0.95,
-  "top_k": 64,
-  "max_output_tokens": 8192,
-  "response_mime_type": "text/plain",
+    "temperature": 1,
+    "top_p": 0.95,
+    "top_k": 64,
+    "max_output_tokens": 8192,
 }
 
 model = genai.GenerativeModel(
-  model_name="gemini-2.0-pro-exp-02-05",
-  generation_config=generation_config,
+    model_name="gemini-pro",
+    generation_config=generation_config,
 )
 
-chat_model = genai.GenerativeModel('"gemini-2.0-pro-exp-02-05"')
+chat_model = genai.GenerativeModel("gemini-pro")
 
-
-# Enhanced CSS for better header styling
+# Enhanced CSS for better styling
 CUSTOM_CSS = '''
 .gradio-container {
     max-width: 1200px !important;
@@ -141,7 +143,6 @@ CUSTOM_CSS = '''
     color: #ffffff !important;
 }
 
-/* Form elements */
 input, select, textarea {
     background: #363636 !important;
     color: #ffffff !important;
@@ -153,7 +154,6 @@ input:focus, select:focus, textarea:focus {
     box-shadow: 0 0 0 2px rgba(52, 152, 219, 0.2) !important;
 }
 
-/* Buttons */
 .action-button {
     background: #3498DB !important;
     color: white !important;
@@ -170,7 +170,6 @@ input:focus, select:focus, textarea:focus {
     box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important;
 }
 
-/* Footer */
 .footer {
     text-align: center !important;
     padding: 20px !important;
@@ -178,129 +177,7 @@ input:focus, select:focus, textarea:focus {
     border-top: 1px solid #404040 !important;
     color: #888888 !important;
 }
-'''
-
-def create_interface():
-    """Create Gradio interface with enhanced UI"""
-    state = SupplyChainState()
-    
-    with gr.Blocks(css=CUSTOM_CSS, title="SupplyChainAI Navigator by Aditya Ratan") as demo:
-        # Enhanced Header
-        with gr.Row(elem_classes="main-header"):
-            with gr.Column():
-                gr.Markdown("# 🚢 SupplyChainAI Navigator", elem_classes="app-title")
-                gr.Markdown("### Intelligent Supply Chain Analysis & Optimization", elem_classes="app-subtitle")
-                gr.Markdown("An AI-powered platform for comprehensive supply chain analytics", elem_classes="app-description")
-                gr.Markdown("Created by Aditya Ratan", elem_classes="creator-info")
-
-.creator-info {
-    color: #3498DB !important;
-    font-size: 1.2em !important;
-    text-align: right !important;
-    margin-top: 10px !important;
-    font-style: italic !important;
-}
-
-.footer {
-    text-align: center !important;
-    padding: 20px !important;
-    margin-top: 40px !important;
-    border-top: 1px solid #404040 !important;
-    color: #888888 !important;
-}
-
-[... rest of the CSS remains the same ...]"""
-
-def create_interface():
-    """Create Gradio interface with enhanced UI"""
-    state = SupplyChainState()
-    
-    with gr.Blocks(css=CUSTOM_CSS, title="SupplyChainAI Navigator") as demo:
-        # Header
-        with gr.Row(elem_classes="main-header"):
-            gr.Markdown("""
-                # 🚢 SupplyChainAI Navigator
-                ### Intelligent Supply Chain Analysis & Optimization
-                An AI-powered platform for comprehensive supply chain analytics
-            """)
-            gr.Markdown("### Created by Aditya Ratan", elem_classes="creator-info")
-        
-        # Rest of the interface components remain the same...
-
-        # Add footer
-        with gr.Row(elem_classes="footer"):
-            gr.Markdown("Designed and Developed by Aditya Ratan | © 2025")
-
-.tab-content {
-    background: #2d2d2d !important;
-    padding: 20px !important;
-    border-radius: 10px !important;
-    box-shadow: 0 2px 4px rgba(0,0,0,0.2) !important;
-    color: #ffffff !important;
-}
-
-.action-button {
-    background: #3498DB !important;
-    color: white !important;
-    border: none !important;
-    padding: 10px 20px !important;
-    border-radius: 5px !important;
-    cursor: pointer !important;
-    transition: all 0.3s ease !important;
-}
-
-.action-button:hover {
-    background: #2980B9 !important;
-    transform: translateY(-2px) !important;
-    box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important;
-}
-
-.status-box {
-    background: #363636 !important;
-    border-left: 4px solid #3498DB !important;
-    padding: 15px !important;
-    margin: 10px 0 !important;
-    border-radius: 0 5px 5px 0 !important;
-    color: #ffffff !important;
-}
-
-.chart-container {
-    background: #2d2d2d !important;
-    padding: 20px !important;
-    border-radius: 10px !important;
-    box-shadow: 0 2px 4px rgba(0,0,0,0.2) !important;
-    color: #ffffff !important;
-}
-
-.chat-container {
-    height: 400px !important;
-    overflow-y: auto !important;
-    border: 1px solid #404040 !important;
-    border-radius: 10px !important;
-    padding: 15px !important;
-    background: #2d2d2d !important;
-    color: #ffffff !important;
-}
-
-.file-upload {
-    border: 2px dashed #404040 !important;
-    border-radius: 10px !important;
-    padding: 20px !important;
-    text-align: center !important;
-    background: #2d2d2d !important;
-    color: #ffffff !important;
-}
-
-.result-box {
-    background: #363636 !important;
-    border: 1px solid #404040 !important;
-    border-radius: 10px !important;
-    padding: 20px !important;
-    margin-top: 15px !important;
-    color: #ffffff !important;
-}
 
-/* Additional dark mode styles */
 .tabs {
     background: #2d2d2d !important;
     border-radius: 10px !important;
@@ -312,21 +189,6 @@ def create_interface():
     color: white !important;
 }
 
-input, select, textarea {
-    background: #363636 !important;
-    color: #ffffff !important;
-    border: 1px solid #404040 !important;
-}
-
-input:focus, select:focus, textarea:focus {
-    border-color: #3498DB !important;
-    box-shadow: 0 0 0 2px rgba(52, 152, 219, 0.2) !important;
-}
-
-.label-text {
-    color: #ffffff !important;
-}
-
 .gr-box {
     background: #2d2d2d !important;
     border: 1px solid #404040 !important;
@@ -354,7 +216,7 @@ input:focus, select:focus, textarea:focus {
     background: #404040 !important;
     color: white !important;
 }
-"""
+'''
 
 class SupplyChainState:
     def __init__(self):
@@ -369,36 +231,10 @@ class SupplyChainState:
         self.model_path = "optimized_xgboost_model.pkl"
         try:
             self.freight_model = joblib.load(self.model_path)
-        except:
-            print(f"Warning: Could not load freight prediction model from {self.model_path}")
+        except Exception as e:
+            print(f"Warning: Could not load freight prediction model from {self.model_path}: {e}")
             self.freight_model = None
 
-def predict_freight_cost(state, weight, line_item_value, cost_per_kg,
-                       shipment_mode, air_charter_weight, ocean_weight, truck_weight,
-                       air_charter_value, ocean_value, truck_value):
-    """Predict freight cost using the loaded model"""
-    if state.freight_model is None:
-        return "Error: Freight prediction model not loaded"
-        
-    features = {
-        'weight (kilograms)': weight,
-        'line item value': line_item_value,
-        'cost per kilogram': cost_per_kg,
-        'shipment mode_Air Charter_weight': air_charter_weight if shipment_mode == "Air" else 0,
-        'shipment mode_Ocean_weight': ocean_weight if shipment_mode == "Ocean" else 0,
-        'shipment mode_Truck_weight': truck_weight if shipment_mode == "Truck" else 0,
-        'shipment mode_Air Charter_line_item_value': air_charter_value if shipment_mode == "Air" else 0,
-        'shipment mode_Ocean_line_item_value': ocean_value if shipment_mode == "Ocean" else 0,
-        'shipment mode_Truck_line_item_value': truck_value if shipment_mode == "Truck" else 0
-    }
-    input_data = pd.DataFrame([features])
-    
-    try:
-        prediction = state.freight_model.predict(input_data)
-        return round(float(prediction[0]), 2)
-    except Exception as e:
-        return f"Error making prediction: {str(e)}"
-
 def process_uploaded_data(state, sales_file, supplier_file, text_data):
     """Process uploaded files and store in state"""
     try:
@@ -433,14 +269,17 @@ def perform_demand_forecasting(state):
         response = model.generate_content(prompt)
         analysis_text = response.text
         
-        # Create an enhanced visualization
+        # Create visualization
         fig = px.line(state.sales_df, title='Historical Sales Data and Forecast')
         fig.update_layout(
-            template='plotly_white',
+            template='plotly_dark',
             title_x=0.5,
             title_font_size=20,
             showlegend=True,
-            hovermode='x'
+            hovermode='x',
+            paper_bgcolor='#2d2d2d',
+            plot_bgcolor='#363636',
+            font=dict(color='white')
         )
         
         return analysis_text, fig, "✅ Analysis completed successfully"
@@ -470,14 +309,17 @@ def perform_risk_assessment(state):
         response = model.generate_content(prompt)
         analysis_text = response.text
 
-        # Create an enhanced risk visualization
+        # Create risk visualization
         fig = px.scatter(state.supplier_df, title='Supplier Risk Assessment')
         fig.update_layout(
-            template='plotly_white',
+            template='plotly_dark',
             title_x=0.5,
             title_font_size=20,
             showlegend=True,
-            hovermode='closest'
+            hovermode='closest',
+            paper_bgcolor='#2d2d2d',
+            plot_bgcolor='#363636',
+            font=dict(color='white')
         )
         
         return analysis_text, fig, "✅ Risk assessment completed"
@@ -526,6 +368,32 @@ def chat_with_navigator(state, message):
     except Exception as e:
         return [(msg_type, msg) for msg_type, msg in state.chat_history] + [("assistant", f"Error: {str(e)}")]
 
+def predict_freight_cost(state, weight, line_item_value, cost_per_kg,
+                       shipment_mode, air_charter_weight, ocean_weight, truck_weight,
+                       air_charter_value, ocean_value, truck_value):
+    """Predict freight cost using the loaded model"""
+    if state.freight_model is None:
+        return "Error: Freight prediction model not loaded"
+        
+    try:
+        features = {
+            'weight (kilograms)': weight,
+            'line item value': line_item_value,
+            'cost per kilogram': cost_per_kg,
+            'shipment mode_Air Charter_weight': air_charter_weight if "Air" in shipment_mode else 0,
+            'shipment mode_Ocean_weight': ocean_weight if "Ocean" in shipment_mode else 0,
+            'shipment mode_Truck_weight': truck_weight if "Truck" in shipment_mode else 0,
+            'shipment mode_Air Charter_line_item_value': air_charter_value if "Air" in shipment_mode else 0,
+            'shipment mode_Ocean_line_item_value': ocean_value if "Ocean" in shipment_mode else 0,
+            'shipment mode_Truck_line_item_value': truck_value if "Truck" in shipment_mode else 0
+        }
+        input_data = pd.DataFrame([features])
+        
+        prediction = state.freight_model.predict(input_data)
+        return round(float(prediction[0]), 2)
+    except Exception as e:
+        return f"Error making prediction: {str(e)}"
+
 def generate_pdf_report(state, analysis_options):
     """Generate PDF report with analysis results"""
     try:
@@ -545,39 +413,42 @@ def generate_pdf_report(state, analysis_options):
             textColor=colors.HexColor('#2C3E50')
         )
         
-        # Add company logo if available
-        # story.append(Image("logo.png", width=100, height=50))
-        
+        # Add title
         story.append(Paragraph("SupplyChainAI Navigator Report", title_style))
         story.append(Spacer(1, 12))
         
+        # Add timestamp
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        story.append(Paragraph(f"Generated on: {timestamp}", styles['Normal']))
+        story.append(Spacer(1, 20))
+        
         # Add executive summary
         story.append(Paragraph("Executive Summary", styles['Heading2']))
-        summary_text = "This report provides a comprehensive analysis of supply chain data..."
+        summary_text = "This report provides a comprehensive analysis of supply chain data, including demand forecasting, risk assessment, and optimization recommendations."
         story.append(Paragraph(summary_text, styles['Normal']))
         story.append(Spacer(1, 20))
         
-        # Analysis results
-        for analysis_type, results in state.analysis_results.items():
-            if analysis_type in analysis_options:
-                story.append(Paragraph(analysis_type, styles['Heading2']))
-                story.append(Spacer(1, 12))
-                story.append(Paragraph(results['text'], styles['Normal']))
-                story.append(Spacer(1, 12))
-                
-                if results.get('figure'):
-                    img_path = os.path.join(temp_dir, f"{analysis_type.lower()}_plot.png")
-                    results['figure'].write_image(img_path)
-                    story.append(Image(img_path))
-                
-                story.append(Spacer(1, 20))
-        
-        # Recent freight predictions
+        # Add analysis results
+        if state.analysis_results:
+            for analysis_type, results in state.analysis_results.items():
+                if analysis_type in analysis_options:
+                    story.append(Paragraph(analysis_type, styles['Heading2']))
+                    story.append(Spacer(1, 12))
+                    story.append(Paragraph(results['text'], styles['Normal']))
+                    story.append(Spacer(1, 12))
+                    
+                    if 'figure' in results:
+                        img_path = os.path.join(temp_dir, f"{analysis_type.lower()}_plot.png")
+                        results['figure'].write_image(img_path)
+                        story.append(Image(img_path, width=400, height=300))
+                    
+                    story.append(Spacer(1, 20))
+        
+        # Add freight predictions if available
         if state.freight_predictions:
             story.append(Paragraph("Recent Freight Cost Predictions", styles['Heading2']))
             story.append(Spacer(1, 12))
             
-            # Create a table for predictions
             pred_data = [["Prediction #", "Cost (USD)"]]
             for i, pred in enumerate(state.freight_predictions[-5:], 1):
                 pred_data.append([f"Prediction {i}", f"${pred:,.2f}"])
@@ -599,14 +470,6 @@ def generate_pdf_report(state, analysis_options):
             story.append(table)
             story.append(Spacer(1, 20))
         
-        # Chat history
-        if state.chat_history:
-            story.append(Paragraph("Recent Chat Interactions", styles['Heading2']))
-            story.append(Spacer(1, 12))
-            for msg_type, msg in state.chat_history[-10:]:
-                story.append(Paragraph(f"{msg_type.title()}: {msg}", styles['Normal']))
-                story.append(Spacer(1, 6))
-        
         # Build PDF
         doc.build(story)
         return pdf_path
@@ -614,6 +477,48 @@ def generate_pdf_report(state, analysis_options):
         print(f"Error generating PDF: {str(e)}")
         return None
 
+def run_analyses(state, choices, sales_file, supplier_file, text_data):
+    """Run selected analyses"""
+    results = []
+    figures = []
+    status_messages = []
+
+    # Process data first
+    process_status = process_uploaded_data(state, sales_file, supplier_file, text_data)
+    if "Error" in process_status:
+        return process_status, None, process_status
+
+    for choice in choices:
+        if "Demand Forecasting" in choice:
+            text, fig, status = perform_demand_forecasting(state)
+            results.append(text)
+            figures.append(fig)
+            status_messages.append(status)
+            if text and fig:
+                state.analysis_results['Demand Forecasting'] = {'text': text, 'figure': fig}
+        
+        elif "Risk Assessment" in choice:
+            text, fig, status = perform_risk_assessment(state)
+            results.append(text)
+            figures.append(fig)
+            status_messages.append(status)
+            if text and fig:
+                state.analysis_results['Risk Assessment'] = {'text': text, 'figure': fig}
+
+    combined_results = "\n\n".join(results)
+    combined_status = "\n".join(status_messages)
+    
+    final_figure = figures[-1] if figures else None
+    
+    return combined_results, final_figure, combined_status
+
+def predict_and_store_freight(state, *args):
+    """Predict freight cost and store the result"""
+    result = predict_freight_cost(state, *args)
+    if isinstance(result, (int, float)):
+        state.freight_predictions.append(result)
+    return result
+
 def create_interface():
     """Create Gradio interface with enhanced UI"""
     state = SupplyChainState()
@@ -621,22 +526,16 @@ def create_interface():
     with gr.Blocks(css=CUSTOM_CSS, title="SupplyChainAI Navigator") as demo:
         # Header
         with gr.Row(elem_classes="main-header"):
-            gr.Markdown("""
-                # 🚢 SupplyChainAI Navigator
-                ### Intelligent Supply Chain Analysis & Optimization
-                An AI-powered platform for comprehensive supply chain analytics
-            """)
+            with gr.Column():
+                gr.Markdown("# 🚢 SupplyChainAI Navigator", elem_classes="app-title")
+                gr.Markdown("### Intelligent Supply Chain Analysis & Optimization", elem_classes="app-subtitle")
+                gr.Markdown("An AI-powered platform for comprehensive supply chain analytics", elem_classes="app-description")
+                gr.Markdown("Created by Aditya Ratan", elem_classes="creator-info")
         
         # Main Content Tabs
         with gr.Tabs() as tabs:
             # Data Upload Tab
             with gr.Tab("📊 Data Upload", elem_classes="tab-content"):
-                with gr.Row():
-                    gr.Markdown("""
-                        ### Upload Your Supply Chain Data
-                        Start by uploading your data files and providing additional context.
-                    """)
-                
                 with gr.Row():
                     with gr.Column(scale=1):
                         sales_data_upload = gr.File(
@@ -668,18 +567,12 @@ def create_interface():
                         elem_classes="action-button"
                     )
             
-            # Analysis Selection Tab
+            # Analysis Tab
             with gr.Tab("🔍 Analysis", elem_classes="tab-content"):
-                with gr.Row():
-                    gr.Markdown("### Select Analysis Types")
-                
                 analysis_options = gr.CheckboxGroup(
                     choices=[
                         "📈 Demand Forecasting",
-                        "⚠️ Risk Assessment",
-                        "📦 Inventory Optimization",
-                        "🤝 Supplier Performance",
-                        "🌿 Sustainability Analysis"
+                        "⚠️ Risk Assessment"
                     ],
                     label="Choose analyses to perform"
                 )
@@ -689,9 +582,7 @@ def create_interface():
                     variant="primary",
                     elem_classes="action-button"
                 )
-            
-            # Results Tab
-            with gr.Tab("📊 Results", elem_classes="tab-content"):
+                
                 with gr.Row():
                     with gr.Column(scale=2):
                         analysis_output = gr.Textbox(
@@ -711,12 +602,6 @@ def create_interface():
             
             # Freight Cost Prediction Tab
             with gr.Tab("💰 Cost Prediction", elem_classes="tab-content"):
-                with gr.Row():
-                    gr.Markdown("""
-                        ### 🚢 Freight Cost Prediction
-                        Estimate shipping costs based on your parameters
-                    """)
-                
                 with gr.Row():
                     shipment_mode = gr.Dropdown(
                         choices=["✈️ Air", "🚢 Ocean", "🚛 Truck"],
@@ -750,53 +635,41 @@ def create_interface():
                             value=50
                         )
                 
-                # Mode-specific values
+                # Mode-specific inputs
                 with gr.Row(visible=False) as air_inputs:
                     air_charter_weight = gr.Slider(
                         label="Air Charter Weight",
                         minimum=0,
-                        maximum=10000,
-                        step=1,
-                        value=0
+                        maximum=10000
                     )
                     air_charter_value = gr.Slider(
                         label="Air Charter Value",
                         minimum=0,
-                        maximum=1000000,
-                        step=1,
-                        value=0
+                        maximum=1000000
                     )
                 
                 with gr.Row(visible=False) as ocean_inputs:
                     ocean_weight = gr.Slider(
                         label="Ocean Weight",
                         minimum=0,
-                        maximum=10000,
-                        step=1,
-                        value=0
+                        maximum=10000
                     )
                     ocean_value = gr.Slider(
                         label="Ocean Value",
                         minimum=0,
-                        maximum=1000000,
-                        step=1,
-                        value=0
+                        maximum=1000000
                     )
                 
                 with gr.Row(visible=False) as truck_inputs:
                     truck_weight = gr.Slider(
                         label="Truck Weight",
                         minimum=0,
-                        maximum=10000,
-                        step=1,
-                        value=0
+                        maximum=10000
                     )
                     truck_value = gr.Slider(
                         label="Truck Value",
                         minimum=0,
-                        maximum=1000000,
-                        step=1,
-                        value=0
+                        maximum=1000000
                     )
                 
                 with gr.Row():
@@ -832,20 +705,18 @@ def create_interface():
             
             # Report Tab
             with gr.Tab("📑 Report", elem_classes="tab-content"):
-                gr.Markdown("""
-                    ### Generate Comprehensive Report
-                    Create a detailed PDF report including all analyses and insights.
-                """)
-                
-                with gr.Row():
-                    report_button = gr.Button(
-                        "📄 Generate Report",
-                        variant="primary",
-                        elem_classes="action-button"
-                    )
-                    report_download = gr.File(
-                        label="Download Report"
-                    )
+                report_button = gr.Button(
+                    "📄 Generate Report",
+                    variant="primary",
+                    elem_classes="action-button"
+                )
+                report_download = gr.File(
+                    label="Download Report"
+                )
+        
+        # Footer
+        with gr.Row(elem_classes="footer"):
+            gr.Markdown("© 2025 SupplyChainAI Navigator")
         
         # Event Handlers
         def update_mode_inputs(mode):
@@ -863,7 +734,7 @@ def create_interface():
         )
         
         analyze_button.click(
-            fn=lambda choices, sales, supplier, text: run_analyses(state, choices, sales, supplier, text),
+            fn=lambda *args: run_analyses(state, *args),
             inputs=[analysis_options, sales_data_upload, supplier_data_upload, text_input_area],
             outputs=[analysis_output, plot_output, raw_output]
         )
@@ -907,3 +778,5 @@ if __name__ == "__main__":
         share=True,
         debug=True
     )
+        
+        # Enhanced title